Harnessing The Power Of Data For Better Tomorrow

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Intelligent Universal Data Exchange for Agriculture Innovation

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Empowering Healthcare AI with Accessible Data

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

IUDX Mobility Unlocking movement Unleashing Growth

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Intelligent Universal Data ExchangeIUDX-Novo 1.0

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Advanced Protection for AgriAI

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Comprehensive Data Framework for State Government AI

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

Continue Reading

Maharashtra Agriculture Data Exchange

Website Abstract: MahaAgX is a secure, interoperable and consent-based agricultural data exchange platform that enables researchers, innovators and policymakers to access diverse agricultural datasets for smarter governance, accelerating innovation, and building Al-powered solutions.

Continue Reading

Government of Maharashtra Launches MahaAgX at AI4Agri 2026: A Public Digital Infrastructure for Scalable Agricultural AI

Mumbai, Feb 22 — The Government of Maharashtra today launched MahaAgX, a state-level agriculture data exchange platform, at AI4Agri 2026 — marking a major step toward building trusted public digital infrastructure for AI-driven agriculture. MahaAgX has been designed and developed by the Centre of Data for Public Good (CDPG), Indian Institute Science, Bangalore, in partnership with the Department of Agriculture, …

Continue Reading

Building Trust in Geospatial Data: A Customer Driven Data Quality Feedback Mechanism for GDI

When shopping online, we rely on product reviews to make informed purchasing decisions. A 4-star rated laptop with hundreds of user reviews gives us confidence about its performance, while a restaurant with consistent positive feedback becomes our go-to choice for dining. But what if the same principle could be applied to geospatial data? This is precisely what the Integrated Geospatial …

Continue Reading